Discovery of magnesium-aluminum alloys by generative model and automatic differentiation approach

Abstract

Abstract Magnesium-aluminum alloy is one of the most common alloy materials in the industry, widely utilized due to its low density and excellent mechanical properties. However, investigating its properties or predicting new structures through experiment inevitably involves complex processes, which cost much time and money. Currently, simulations, such as density functional theory (DFT) and machine learning (ML) methods, are mainly employed for predicting new alloy materials. While precise, DFT incurs significant computational costs, which posing challenges for research. On the other hand, although ML methods are versatile and efficient, they demand high-quality datasets and may exhibit some degree of inaccuracy. To address these challenges, we employ a combination of generative model and automatic differentiation, reducing the search space and accelerating the screening of target materials. Finally, we use generative model to predict a multitude of magnesium-aluminum alloys. We perform structure optimization and property evaluation for ten potentially valuable intermetallic compounds. Ultimately, we identified Mg3Al3, Mg2Al6, Mg4Al12, Mg15Al, and Mg14Al2 as five stable structures, among which Mg4Al12, Mg15Al and Mg14Al2 may have higher potential application value.